Towards Artificial (General) Intelligence

In a previous post on the topic of artificial intelligence (AI), about a year ago, I argued that apps like ChapGPT were neither artificial nor intelligent. These large language models (LLMs) were basically a rear view mirror into what is posted on the web. LLMs learn how words are related to one another and essentially develop answers based on these patterns and relationships. Well, it is really a bit more sophisticated than that.

Current business interest in AI is high, with a significant 64% of businesses believing that artificial intelligence will help increase overall productivity, and many of these businesses are applying AI to improve production processes, automate tasks, and develop new products or services. The AI industry is actively pursuing the goal of artificial general intelligence (AGI), with major companies like OpenAI, Google DeepMind, and Anthropic leading the charge. AGI, also known as strong AI, refers to a form of artificial intelligence that could perform intellectual tasks that humans can, thus mimicking human intelligence without human intervention. As I mentioned, LLMs initially focused on the relationship between words, using methods like word embedding to understand context and meaning based on proximity and frequency of use. These early models often struggled with more complex tasks, as they could not fully grasp nuanced meanings and implications of language.

A recent article by Ryan Daws in TechForge describes a new methodology introduced by researchers from Google DeepMind and the University of Southern California. This methodology, called SELF-DISCOVER, is designed to significantly improve the reasoning capabilities of LLMs (RLLM) when tackling complex problems. This evolution can be depicted by the diagram below.

In the last couple of years, advances in machine learning and natural language processing have allowed for the development of more sophisticated models that can identify concepts, understand context, and even reason to some extent. These large language models, trained on vast amounts of text data, can generate human-like responses and answer complex queries. Yet, it is important to note that while these models are advanced, they still fall short of truly “thinking” like humans. They don’t possess consciousness or an understanding of the world. They still operate based on patterns and correlations in the data they were trained on.

Reasoning in Large Language Models (RLLM)

“Reasoning” is the process of making sense of information, drawing conclusions, and making decisions or predictions. Just like humans reason through problems or questions, developers want AI to do the same. This starts by recognizing concepts within the input data to understand their meaning, and then use this understanding to generate appropriate responses or actions. ”Reasoning in Large Language Models” (RLLM) is about developing solving strategies to understand the question, figure out the concepts involved, what information is needed to build the answer, and then generate an appropriate response. These solving strategies are increasingly based on the combined use of sophisticated atomic reasoning modules as part of overall “AI reasoners”. The framework operates in two stages:

• Stage one involves composing a coherent reasoning structure specific to the task, and leveraging a set of “atomic “reasoning modules and task examples.
• During decoding, LLMs then follow this self-discovered structure to arrive at the final solution. Decoding refers to the process of transforming the output of a model – typically sequences of numbers for language models – back into human-readable text.

Atomic Reasoning Modules

“Atomic” in “atomic reasoning modules” refers to the idea of breaking down complex problems or tasks into smaller, manageable parts – or “atoms”. Each atom represents a fundamental unit of reasoning or a single step in a process. For instance, if an AI system is trained to understand the concept of “weather”, it should be able to identify related elements like temperature, precipitation, wind speed, etc., and know how they interrelate. If asked about the weather forecast, the AI should be able to infer from the concept of “rain” that it might not be a good day for outdoor activities. A crucial AI piece of this puzzle is the critical thinking atomic reasoning module which enables AI systems to tackle more complex problems, adapt and make judgments on their own, and ultimately better mimic human reasoning. The module helps:

• Analyze information by breaking down complex data into smaller parts to understand it better,
• Evaluate evidence by assess the reliability and relevance of the information it has,
• Understand context by considering background information to make sense of the data,
• Identify biases by avoiding jumping to conclusions based on incomplete or biased information, and
• Make decisions by evaluating the pros and cons of different options to develop the best recommendations.

Results

The RLLM framework described in Daws’ article shows up to a 32% performance increase compared to traditional methods like Chain of Thought (CoT) and promises the ability to tackle challenging reasoning tasks, answer questions more accurately, and document the reasoning process considered a significant step towards achieving artificial general intelligence (AGI). Of course, it is important to remember that while these models can seem very smart, their ‘reasoning’ is based on patterns they have learned from lots of data, not on creative thinking. And just like humans, these models can come to the wrong inferences using inaccurate or biased data and make mistakes!.

In Summary

Beyond performance gains, the DeepMind and University of Southern California research suggests more advanced problem-solving capabilities aligned with human reasoning patterns. The evolution of AI continues, and while we are not at artificial general intelligence yet, we are inching closer to creating AI models that can reason and interact to provide more accurate, useful, and nuanced results.

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Artificial? Intelligence?

Welcome to 2023! AI (Artificial Intelligence) is in the news these days in part because of GPT-3. GPT-3 is a natural language processing (NLP) engine coupled with a deep learning model built by the San-Francisco-based OPEN-AI startup company. The GPT-3 model follows the GPT-2 model introduced in 2019. The GPT-2 model includes 1.5 billion parameters while the GPT-3 model includes 175 billion parameters. The size of the GPT-3 model greatly increases its ability to capture language and ideas at a conceptual level, meaning at the essence of the ideas being expressed. The OPEN-AI deep learning model uses the concept of “attention” to create better correlations between words. These correlations then enable the GPT2-3 models to better “understand” the phrases being examined.

Comparison of all available language models (LMs) parameter wise
Source: TowardsDataScience

There are essentially three important components in GPT-3:

• Older models created relationships between words generally based on their proximity. These models use attention and other metrics to create more sophisticated relationships akin to concepts. By the way, the notion of concept is one that has not been studied very much this far…
• The size of the model enables the identification of many different concepts based on context. For instance, the word “foundation” has several meanings including the component of a physical building, the underpinning of an argument, an institution, and others. This model is the culmination (so far) of 70 years of research from different fields, including the 1950 publication of Alan Turing’s “Computing Machinery and Intelligence” paper in the journal “Mind.”
• The development of this model is made in part possible by relatively cheep computing power and data storage. This is also facilitated by the compilation of open web crawl data repositories such as Common Crawl which contained 3.35 billion web pages in its November 26 – December 10, 2022 archive.

Deep Learning Use Cases

The main uses of deep learning models include image recognition, translation, recommendation engines, and, in this case, natural language. The OPEN-AI startup has essentially created a platform business by enabling developers to access the GPT-3 model through an application programming interface (API) and create their own application. See here for a number of apps.

Use cases for natural language deep learning models include:

• Generate natural language documents based on an analysis of concepts found in the GTP-3 database. The user provides key words, the model searches for related concepts and ideas, and composes text based on these ideas,
• Synthesizing ideas from various sources. For instance, analyzing customer feedback or comments to identify themes and provide an overall understanding of the meaning of this feedback,
• Replace humans in customer service chats by understanding questions or comments and creating more relevant answers,
• Creating real-time evolving story lines in games for instance, based on interactions with users,
• Providing recommendations to consumers by analyzing their historical purchases and other characteristics,
• Copyright marketing documents, blogs (not this one…,) essays,
• etc.

Benefits and Issues

Benefits of GPT-3-type models include high quality automated content creation tailored to a specific situation or need. This is particularly beneficial in the marketing field because specific recommendations, or even customer-specific advertisements, can be developed in real time. Other benefits will be to improve search results by leveraging conceptual meaning found in existing documents (semantic search,) assistive customer service, education (language and technical, special education,) and idea generation…

These benefits can be roughly divided into two categories: increased productivity (replacing humans by competent computers,) and computer recommendations and content creation. This second type of benefit can also be considered an improvement in productivity but it brings digital capabilities closer to creativity which is a capability that we considered hitherto limited to the human sphere. In mid-2020, Gwern Branwen experimented with GPT-3 poetry and commented: “GPT-3’s samples are not just close to human level: they are creative, witty, deep, meta, and often beautiful. They demonstrate an ability to handle abstractions, like style parodies.”

Language-based automation and digital recommendations are not new. Siri and Alexa understand human language and respond – for the most part – adequately. The ability to understand text at a conceptual level, however, brings these capabilities to a different level. Bias is one important issue. GPT-3 and other models that synthesize prior academic or Internet material may bring racial, gender, or other biases that may be contained in the material. Another form of bias is the inclusion of false facts in the analysis because the model cannot (yet) assert what is or may be true or false.

At this stage, these models can be blind and unpredictable as it is not always possible to understand how these large and complex transformer models arrive at certain conclusions. The models intrinsically assume that the language they analyze is logical, grammatically correct, and otherwise free of linguistic errors. Researchers and companies such as Anthropic are trying to address these problems. The question is: what type of interventions are necessary to “improve” the results of these models, what does “improve” mean, and are improvements algorithmic-based or performed by humans (such as reinforcement learning.)

Bottom Line…

• Is it artificial? Not really! GTP-3 and other similar models are looking in the mirror at existing written material and trying to understand its true meaning.
• Is it intelligence? Yes! In a way, the models are bringing new capabilities in analyzing natural language. In fact, the models seem to be able to manage complexity by digitizing critical thinking and attempting to focus on key concepts in the material.

We all have trouble dealing with complex issues surrounding us and the use of these models can definitely help boil down the complexity of these issues. The key, of course, is to ask the model questions with the requisite amount of specificity. The potential commercial benefits of this technology are fueling new developments in the application sphere, but also in the research fields of linguistics, biology, neural network modeling, conceptual research, and more. These models, however, are unlikely to replace true creativity, using imagination or original ideas. But that will be the topic of another blog…

Of Interest

• Andriy Myachykov, Michael I. Posner, CHAPTER 53 – Attention in Language, Editor(s): Laurent Itti, Geraint Rees, John K. Tsotsos, Neurobiology of Attention, Academic Press, 2005, Pages 324-329, ISBN 9780123757319, https://doi.org/10.1016/B978-012375731-9/50057-4. (PDF) Attention in Language (researchgate.net)
• Polysemanticity and Capacity in Neural Networks by Buck Shlegeris, Adam Jermyn, Kshitij Sachan
• A commentary of GPT-3 in MIT Technology Review 2021
• Watch Two AIs Reflect On The Nature of Complexity. (GPT-3)

Three skills for dealing with complexity

Cave Rescue: A lesson in leadership

July 10, 2018

The following weeks will likely reveal the intricacies of the rescue of the youth soccer team from the Chiang Rai cave in Thailand.  But from the outside, it seems to me that the operation is a clear example of sucessful leadership in a complex environment.

• The area’s acting Governor Narongsak Osatanakorn, personally took charge of the operation, quietly and effectively,
• He quickly assembled a team of specialists from many different areas: speleology, health, metereology, parent relations, etc.
• The team invited outside experts and volunteers into the process but clearly remaining in control,
• The team evaluated alternative strategies for the rescue, taking input from every discipline,
• Meanwhile, and without skipping a beat, a gigantic logistics effort went underway, procuring and deploying all types of equipment and people from pumps to ropes, oxygen, ambulances, food, as well as personnel and goods to support the primary teams,
• The top team established policies that were stronly implemented:
  • Privacy comes to mind.  Photos of the rescued kids inside or outside the cave may have been taken but they will not likely be released until all are sucessfully out.  The press was not allowed near operational areas.
  • Parent relations seems to be handled very well.  All parents are on board with the team’s approaches and policies.
  • There is a news blackout of sorts, to the benefit of rescuers and victims.  Politicians are not exploiting the trajedy by parading in front of cameras.
• Finally, the team has shown the ability to take advantage of changing circumstances by adapting the rescue plan when advantageous.  For instance, the second rescue operation was implemented hours earlier than originally planned because oxygen was resupplied faster than planned.
• This is a clear sign of good leadership:
  • A tactical plan is in place to support the mission.
  • The core team is in constant communications, discussing and re-evaluating execution of that plan, and
  • The leader is constantly updated and is willing to take decisive action.
    

    Bravo!, a lesson to be learned!

Complexity, Reliability And Cost

Reprinted from Semiconductor Engineering 

JUNE 14TH, 2018 – BY: ED SPERLING

Fraunhofer EAS’s top scientist digs into new technical and business challenges shaping the semiconductor industry.

Peter Schneider, director of Fraunhofer’s Engineering of Adaptive Systems Division, sat down with Semiconductor Engineering to talk about future challenges in complexity, time to market and reliability issues, advanced packaging architectures, and the impact of billions of connected devices. What follows are excerpts of that discussion.

SE: What is the biggest challenge you see in the semiconductor industry?

Schneider: Complexity. You have complexity in the system, in the structure, in the function, in the value chain, and in customer supplier relations. Supporting people to design systems, to make them, to bring them to market more quickly, is the most important problem we’re dealing with right now.

SE: And not everything is moving at the same pace. That causes it’s own set of issues, right?

Schneider: Yes. When we’re talking to German authorities about research projects, on one side we have all of these amazing things such as IoT and blockchains. And then we have all of these regulations and slow-moving decision processes. It takes a long time to get decisions in a fast-moving market.

SE: There’s a push to speed up time-to-market everywhere. But at the same time we’re seeing increased pressure to improve reliability and security in devices, while also customizing them. Is that possible?

Schneider: We’ve certainly seen this with Industry 4.0. Everybody’s talking about shorter time-to-market and customized products. But if you have individual products with specific features and safety regulations, the pieces don’t fit together. You have to do safety testing and you have to get certifications. You need a sort of generic safety certification for individual products.

SE: How do you allocate resources internally when different parts are moving at different speeds?

Schneider: Our strength in the Institute is interdisciplinary work. It’s a mixture of different disciplines and qualifications, and that’s the way we try to adapt to the market, too. But it’s very difficult.

SE: Are you seeing more devices starting to incorporate the level of reliability you would apply to functional safety?

Schneider: Not everywhere. That level of reliability is not yet present in the smartphone or tablet market. But these devices are rapidly getting involved in the human-machine interface. If you start to control the machines with a smartphone or tablet, that will involve functional safety. So if you look at the new robotic cars with much larger flat screens, if you have air conditioning or a radio being controlled by these devices, that’s no problem. But if you adapt your steering or suspension system, it might become a safety issue if you can’t change it. You you might be in an off-road mode, and if you have to speed up then you can’t do it. So you have to incorporate the behavior of consumer electronics into the control system of the cars. You might have to block some driving modes, for example. Sometimes you focus on functional safety, and security and reliability come afterward. At first things are done to get them working, and then you have an issue that needs to be resolved.

SE: The business fundamentals are beginning to shift, too. The phrase we hear repeatedly is ‘mass customization,’ because you can’t develop one chip anymore and expect to sell billions of units. How are you approaching this?

Schneider: We’re trying to build multi-block systems. These are based on platforms that make these systems easier to build, using multiple blocks that are proven and which can be put together on multiple levels. That starts with the SSD in the middle, over the first level of a package, and over additional components like sensors. This could be one approach, and these platforms obviously could include safety and security, as well.

SE: This is more than just chiplets, right? It’s a system-level platform.

Schneider: Yes, it’s integration at higher levels. You can configure at the chip level, you can have things at the package level, and you can put this on a PCB or an interposer with additional building blocks and do the customization at all these different levels.

SE: Does this use hard IP? Full chips?

Schneider: In the first step it’s hard IP, but you can add in different things. It goes along with our intelligent IP approach.

SE: What’s the interconnect for all of that?

Schneider: We use standard communications interfaces because it has to be connected to the environment it’s running in. So if you build a single sensor device with a very specific function and form factor, it has to communicate with higher levels, whether that’s the edge or the cloud.

SE: What does Fraunhofer develop for all of this?

Schneider: We build hard and soft IPs. We also build systems at the package level. So we help customers in their areas provide the right systems integration.

SE: For all configurations?

Schneider: It’s whatever the customer wants. We’ve had methods and experience to build real 3D systems for 20 years. We had a research project in the beginning where TSVs came up, and we had a close collaboration with technology companies that were trying to do deep-trench etching and filling. That was complemented by design methods using multi-physics simulations developing tool extensions and workaround to handle 3D data in the system.

SE: So let’s roll back here a bit. Is there a base platform that works with a lot of different solutions?

Schneider: We start at a point where we have a lot of background. So if you look at sensors, for example, we’ve made a universal sensor platform. We don’t start with power modules. We use data acquisition of signal processing, and then do feature extraction or algorithms you can integrate into an intelligent sensor system. The main goal is to enable multiple sensor systems, so you can do sensor data fusion and send that data to the IoT system. The key factor is to separate the problem into basic functional blocks, so you have sensing, a control system, distributed algorithms, and then you have actuators with power devices for influencing the entire process.

SE: So the starting point wasn’t shrinking features down to 7nm. It was the common thread, right?

Schneider: Yes.

SE: That’s a different approach than most companies have taken.

Schneider: It’s a customer-centric approach. When we talk to customers about the new technologies and good ideas, they are very curious about what they can do, and then the question what are the benefits, the liabilities and costs. Small and medium-sized companies are very conservative because they don’t have the resources to try a lot of things. That’s why it’s hard to get companies to move to 3D integration.

SE: So you’ve got some advanced research, but some of your customers aren’t necessarily the ones that are at the leading edge. How do those worlds mesh?

Schneider: A lot of this is what we gain from collaboration with big companies. We collaborate on problems such as thermal, electrical interactions, verification, system-level simulation, semiconductor reliability. We solve these problems and we have good success with big companies, and then we try to translate it for small- to medium-sized enterprises. ‘If you do this, then you shouldn’t do this.’ It’s a benefit for everyone. If you’re a consultant for a small company, they might be selling a high-end device to a semiconductor company, and a large semiconductor company might have concerns for niche application markets.

SE: How does security fit in here. It’s becoming a bigger problem as more devices are connected.

Schneider: From our perspective, it’s absolutely necessary. It has to be an integral part of IoT devices. There is another Fraunhofer Institute in Munich that deals with these issues. We have a very close relationship with them. On the level of physically unclonable functions, we have trust anchors. We have secure drivers on different levels. And you need a secure infrastructure to deal with all of these things. If you use IoT platforms for device management, you need to do it in a secure way. And if you have self-configuring IoT systems and you add 10,000 nodes into the environment, you have to deal with all of the possible interactions.

SE: What other challenges do you see on the horizon?

Schneider: Lifetime management in connected systems. You have a lot of nodes, you bring those to a new environment, and you have to reconfigure them. So you have an application connected to other systems you have, and you cannot affect the entire system. The application has to work within the digital infrastructure that is there.

SE: Plus you’ve got backward-compatibility issues, right?

Schneider: Yes, that’s one concern, but it’s generally not disrupting what you have. But with billions of devices, how are you going to configure all of them? If you try to get into your WiFi router today, you get into trouble if you don’t have the right password for one device. Now imagine billions of these devices. I’m sure we will overcome these problems, but this time it will likely take longer than expected.

***

The Guardian view on internet security: complexity is vulnerable

Reprinted from The Guardian 

A huge weakness in wifi security erodes online privacy. But the real challenge is designing with human shortcomings in mind

This week’s security scandal is the discovery that every household with wifi in this country has a network that isn’t really private. For 13 years a weakness has lurked in the supposedly secure way in which wireless networks carry our information. Although the WPA2 security scheme was supposed to be mathematically proven to be uncrackable, it turns out that the mechanism by which it can compensate for weak signals can be compromised, and when that happens it might as well be unencrypted. Practically every router, every laptop and every mobile phone in the world is now potentially exposed. As the Belgian researcher who discovered the vulnerability points out, this could be abused to steal information such as credit card numbers, emails and photos.

It is not a catastrophic flaw: the attacker has to be within range of the wifi they are attacking. Most email and chat guarded by end-to-end encryption is still protected from eavesdroppers. But the flaw affects a huge number of devices, many of which will never be updated to address it. Since both ends of a wifi connection need to be brought up to date to be fixed, it is no longer safe to assume that any wifi connection is entirely private.

The story is a reminder of just how much we all now rely on the hidden machineries of software engineering in our everyday lives, and just how complex these complexities are. The fact that it took 13 years for this weakness to be found and publicised shows that no one entirely understands the systems that we all now take for granted. Also this week, a flaw was discovered in one of the widely used chips that are supposed to produce the gigantic and completely random numbers which are needed to make strong encryption truly unbreakable. Even the anti-virus systems that many users hope will protect them can be turned inside out. First the Israeli and then the Russian intelligence agencies appear to have penetrated the Russian-made Kaspersky Anti-Virus, a program of the sort which must have access to all the most sensitive information on a computer to perform its functions.

And then there are the known unknowns: the devices which most users do not even notice are connected to the net. It is estimated that there will be 21bn things connected to the internet by 2020, from baby monitors and door locks to cars and fridges. Billions of these are unprotected and will remain that way.

But this kind of technological failure should not blind us to the real dangersof the digital world, which are social and political. The information about ourselves that we freely give away on social media, or on dating sites, is far more comprehensive, and far more potentially damaging, than anything which could be picked up by a lurking wifi hacker. The leak of millions of user accounts from Equifax, the credit reference agency, is only the most recent example of the plundering of personal information by criminals.

Such hacks might be regarded as the outcome of technical wizardry, but are dependent on human shortcomings in recognising and fixing security flaws. Others would be impossible without tricking real users out of their passwords first. In criminal hands, social engineering beats software engineering every time, and the problems of the internet cannot entirely be solved by technical means. Until we design for human nature, no perfection of machinery can save us.

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How Network Complexity Killed Water Cooler Collaboration

by Grant Ho | NetBrain Technologies

Reprinted from The VARGuy.com

Effective collaboration could once be defined as hanging around the company water cooler discussing the latest network issues and emerging trends. Thanks to the ever-increasing complexity levels and scale of today’s networks, however, these casual conversations can no longer be classified as an effective method of information sharing. As network size and complexity increase, so do network teams. Enterprise networks are no longer operated by small teams in a single location, but teams of varying technical skills and diverse geographies. If network teams want to be on the same page as the rest of their IT counterparts with the ability to respond quickly to a network issue, new forms of collaboration and information sharing are required.

Out of sight, out of mind?

The new era of collaboration requires an effective strategy that ensures vital information is shared across teams. However, in a recent NetBrain survey, 72 percent of network engineers cited a lack of collaboration between teams, specifically network and security teams, as the number one challenge when mitigating an attack. Due to increasing network complexity, these teams have become more siloed, making ongoing communication difficult. This becomes problematic when a network outage arises and teams don’t know how to jointly respond as they have little to no experience working together. The result? Hours wasted on communicating issues that should be standard procedure rather than swiftly addressing and repairing the problem.

Many network teams are combatting this issue with a multi-phase approach to improve collaboration, process and tools. When it comes to the network, automation is a critical enabler for all stakeholders by providing the ability to share domain expertise and operational data during network problems.

Democratize knowledge

The simplest form of collaboration is knowledge-sharing. This means making sure that everyone tasked with managing the network is equipped with the appropriate information to perform their job optimally. While it seems simple, the approach can be a significant challenge for any enterprise network team.

Today, teams struggle to document and share knowledge as the process is time consuming and tedious. This limits the ability to scale as critical network information is often stored in the brains or hard drives of tribal leaders who have worked on a specific network for many years. The domain knowledge is far too deep. While tribal leaders have spent years honing their skills and learning the ins and outs of their networks, organizations can be at an advantage by ensuring more network engineers are equipped with similar levels of information. For instance, what happens when a busy, senior Level-3 engineer isn’t around to troubleshoot a network outage? Democratizing her best practices so that more junior engineers (i.e., Level-1 and Level-2 engineers) can diagnose the problem, instead of waiting and escalating all the way to the Level-3 engineer, can result in quicker response times and better SLAs.

Streamline data sharing

While sharing best practices is critical, collaboration is more than just a clear picture of how to do the work. Sharing is also crucial at the task level where insights and conclusions should be made as a team. However, organizations often struggle with this process—many network teams communicate via email or web conference, and here, data sharing becomes cumbersome and comes in log files or data dumps.

Drawing key insights and actionable decisions from a data dump is difficult. Even if an individual has the right insight he or she needs for the task at hand, it can be time consuming and tedious to work through. These manual methods of data collection and sharing (e.g., box-by-box, screen scraping or legacy home-grown scripts) result in slower troubleshooting and a longer mean time to repair (MTTR). Take the example in a typical network operations center. Here a high degree of redundant work can happen as Level-3 engineers often have to repeat the same tasks as Level-2 engineers, and Level-2 engineers have to do the same with Level-1 engineers. The culprit is largely a poor flow of information disguised as incomplete documentation at best and incorrect documentation at worst. Instead, by providing network teams with a common visual interface—for instance, a map of the network’s problem area—they can access the most relevant data while utilizing shared insights to accelerate decision-making.

Security through collaboration and automation

While collaboration is critical to network troubleshooting, it becomes particularly essential when the network comes under attack. During a security incident, the network team typically works with the security team, the applications team, and related managers. With so many stakeholders involved, centralized information becomes imperative. That’s why it’s critical to democratize best practices and seamlessly share information to drive shorter repair times and better proactive security.

Again, automation plays a key role. For instance, by automating the creation of the exact attack path, network and security teams can quickly get on the same page by gaining instant visibility into the problem. Moreover, when diagnosing the problem, automating best practices contained in off-the-shelf playbooks, guides, and security checklists is essential. Digitizing those steps into runbooks that can be automatically executed—and capturing runbook insights so they can be shared across network and security teams—results in faster responses and less human error. As shown in the graphic, these runbooks can then be enhanced with lessons learned from the security event to improve responses down the road. As networks are increasingly at risk, organizations that learn from the past to improve their future will be at an advantage when it comes to mitigating future threats.

The bottom-line is that the scale and complexity of networks is changing how organizations respond to network issues and security threats. Automating critical data-sharing will foster better collaboration and results than the water cooler ever did.

About the Author

Grant Ho is an SVP at NetBrain Technologies, provider of the industry’s leading network automation platform. At NetBrain, he helps lead the companies’ strategy and execution, with a focus on products, events, content and more. Prior to joining NetBrain, Grant held various leadership roles in the healthcare IT industry and began his career as a strategy consultant to wireless and enterprise software companies. You can follow Grant on Twitter @grantho and you can follow NetBrain @NetBrainTechies.

Complexity and AR (Augmented Reality)

I have commented on this blog on several occasions about the fact that we are living in an increasingly complex world.  Fields of knowledge, from medicine to social sciences, to many others, are ever-expanding.  According to a 2013 IBM article by Ralph Jacobson,  2.5 quintillion bytes of data are created every day. This data comes from sensors, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few.  Connections and linkages between data points, the true source of complexity, are also expanding.  Google can link people to places through their phones, and how long they stayed at each place.  Amazon, Facebook, and Google understand people and their interests based on data they collect through interactions.

The proliferation of data and how they relate to one another makes it more and more difficult for us to find and understand what we need to know, and how to make sound decisions.  We need tools to help us take advantage of this data and inform and educate us.  This is where AR (augmented reality) comes in.

AR is a relatively new concept seeking to overlay digital components on top of q real scene.  This can be done through viewing glasses or a screen where objects or information is presented on top of a live or still view.  Large companies like Facebook, Google, Apple and Microsoft are each embracing this general idea with different objectives and perspectives.

I came across postings by Luke Wroblewski (LinkedIn), a product director at Google. Luke has begun to describe a conceptual approach to AR where the digital overlays are designed to serve specific functions by leveraging contextual data (in this instance, data known to Google.)  In this example, the AR algorithm would have (this is a mock-up) recognized that the driver needs to find a gas station. The AR platform would then overlay the respective price differential and the distance of alternative stations to the one in sight.  To me, this is a great example of how AR can help manage complexity.  The AR platform would distill the inherent relationship between cost and distance.  This relationship between cost and distance is at the heart of the “complex” decision that the driver must take:  What are the risks of driving further to save money?  Do I have time? Do I have enough gas in the tank?  Do I really know how little gas is in the tank?

Wroblewski and Google are onto something here.  “Representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multi-resolution data across application domains” to quote Meyer Z. Pesenson at Al. in their paper “The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch.”

AR may be more than a hammer in search of a nail. It may be a new conceptual approach to help us deal with big data and its complexity.